Recommendation systems blackare typically measured in either blackan online (A/B testing or interleaving) or blackan offline manner. However, the offline evaluation of recommender systems remains the most widely reported evaluation in the literature blackdue to the difficulties in deploying research systems for online evaluation [1]}, [2]}. In reality, it is hard to blackreliably evaluate the effectiveness of a recommendation model in an offline fashion using logs of historical user interactions [3]}, [4]}, [5]}. The issue is that there may be confounders, i.e. variables that affect both the exposure of items and the outcomes (e.g. ratings) [6]}. Confounding blackproblems occur when a platform attempts to model user behaviour without taking into account the blackselection bias in the exposed recommended items [7]}, leading to unintended consequences. In this situation, it is difficult to distinguish which user interactions stem from blackthe users' true preferences and which are influenced by the blackdeployed recommender system.
